The problem NeuraNFT solves

We are addressing several key challenges that currently inhibits growth in the AI industry:

1. Data Ownership and Privacy
   In the current AI ecosystem, users often lose control over their data to centralized platforms, which monopolize valuable AI models. This limits users' ability to participate meaningfully in the AI economy, raising concerns around data privacy and unauthorized use. Neuranft enables users to have absolute ownership over their assets and also give them the ability to manage the usage of their assets on the platform. We have also utilised Quicknode’s function to create a telegram bot that gives the owners real time updates on how their assets are being used on our platform.

2. Fair Monetization for Independent Creators
   Small AI developers struggle to generate revenue due to the dominance of centralized platforms that charge high fees and expose creators to IP risks. Many talented individuals and smaller companies cannot fairly monetize their innovations, limiting the potential for grassroots AI solutions. We solved this issue by creating a platform where users can monetize their assets independently and also used Quicknode’s stream to provide an additional dashboard where they can track and monetize their assets effectively.

3. Limited Access to AI Assets
   AI resources, including datasets and models, are not easily accessible to startups, small businesses, researchers, and students. Without affordable and open access to these resources, many communities are excluded from participating in AI development, stifling creativity and innovation.

4. Trust Issues with Centralized Platforms
   Centralized systems are often opaque and untrustworthy, with complicated IP management processes that deter businesses from fully embracing AI solutions. Lack of transparency makes collaboration difficult and raises IP infringement concerns, reducing the willingness of developers to host or share their models.

Challenges we ran into

Throughout the development of NeuraNFT, we encountered several challenges that required innovative solutions. One key issue was smart contract fee optimization, where we needed to streamline operations to minimize gas costs without compromising the platform’s functionality. To address this, we refined the logic and storage structure of our smart contracts, ensuring that transactions remained affordable for all users. To store data and index it efficently we initially needed to create additional data structures in the contract which inturn increased the gas fee. By integrating quicknode streams and functions with database we managed to mitigate this.

Before using QuickNode's Streams and Function it was very difficult to create an application that provided the correct represensation of users asset on our platform which was a crucial part of our application. This was because traditional polling mechanisms were not only resource-intensive but also resulted in high latency, increased server costs, and frequently missed critical state changes during network congestion.

However, after integrating QuickNode Streams and Functions, we transformed this inefficient polling system into a streamlined, event-driven architecture. The new implementation provides real-time data streaming with minimal latency thus providing users with real time data analysis to manage their assets effectively.

Another major challenge we encountered was the inability to host HPC nodes instances on AWS due to budget constraints and high infrastructure costs. This limitation significantly impacted our ability to provide users with direct model chat access for demonstration purposes. While we've successfully optimized other aspects of our infrastructure, this GPU-intensive component remains a temporary hurdle. However, we're actively working on gathering resources. Once we secure adequate funding, we plan to implement a robust GPU infrastructure that will allow users to interact with our AI model.